Bohao Tian, Shijun Zhang, Sirui Chen, Yuru Zhang, Kaiping Peng, Hongxing Zhang, Dangxiao Wang

Flow, an optimal mental state merging action and awareness, significantly impacts performance, emotion and wellbeing in real-world contexts. However, capturing its fluctuations on a sub-minute timescale is challenging due to the sparsity of the existing flow measuring tools. Here we present a virtual reality fine fingertip force control (F3C) task to induce flow, wherein the task challenge is set at a compatible level with personal skill, and to track the flow fluctuations from the synchronous force control performance. We extract eight performance metrics from the fingertip force sequence and reveal their significant differences under distinct flow states. Further, we built a flow decoder and demonstrated that the flow variations can be decoded using selected metrics. The predicted values reach significant correlation with the self-reported flow intensity (r=0.81). This study showcases the feasibility of tracking intrinsic flow variations with high temporal resolution using task performance measures.